U.S. patent number 10,809,402 [Application Number 15/641,916] was granted by the patent office on 2020-10-20 for non-uniform optimal survey design principles.
This patent grant is currently assigned to ConocoPhillips Company. The grantee listed for this patent is CONOCOPHILLIPS COMPANY. Invention is credited to Frank D. Janiszewski, Chengbo Li, Charles C. Mosher, Laurence S. Williams.
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United States Patent |
10,809,402 |
Li , et al. |
October 20, 2020 |
Non-uniform optimal survey design principles
Abstract
Method for acquiring seismic data is described. The method
includes determining a non-uniform optimal sampling design that
includes a compressive sensing sampling grid. Placing a plurality
of source lines or receiver lines at a non-uniform optimal line
interval. Placing a plurality of receivers or nodes at a
non-uniform optimal receiver interval. Towing a plurality of
streamers attached to a vessel, wherein the plurality of streamers
is spaced apart at non-uniform optimal intervals based on the
compressive sensing sampling grid. Firing a plurality of shots from
one or more seismic sources at non-uniform optimal shot intervals.
Acquiring seismic data via the plurality of receivers or nodes.
Inventors: |
Li; Chengbo (Houston, TX),
Mosher; Charles C. (Houston, TX), Janiszewski; Frank D.
(Houston, TX), Williams; Laurence S. (Houston, TX) |
Applicant: |
Name |
City |
State |
Country |
Type |
CONOCOPHILLIPS COMPANY |
Houston |
TX |
US |
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Assignee: |
ConocoPhillips Company
(Houston, TX)
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Family
ID: |
64271568 |
Appl.
No.: |
15/641,916 |
Filed: |
July 5, 2017 |
Prior Publication Data
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Document
Identifier |
Publication Date |
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US 20180335536 A1 |
Nov 22, 2018 |
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Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
Issue Date |
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62506859 |
May 16, 2017 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G01V
1/368 (20130101); G01V 1/3808 (20130101); G01V
2210/324 (20130101) |
Current International
Class: |
G01V
1/28 (20060101); G01V 1/36 (20060101); G01V
1/00 (20060101); G01V 1/38 (20060101) |
Field of
Search: |
;367/15 |
References Cited
[Referenced By]
U.S. Patent Documents
Foreign Patent Documents
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WO |
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WO2011156491 |
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Dec 2011 |
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WO |
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May 2015 |
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WO |
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2016009270 |
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Jan 2016 |
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WO |
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2018085567 |
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May 2018 |
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WO |
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Other References
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Lumpur, Malaysia, Mar. 25-28, 2014, 4 pgs. cited by applicant .
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Primary Examiner: Murphy; Daniel L
Attorney, Agent or Firm: Polsinelli PC
Parent Case Text
CROSS-REFERENCE TO RELATED APPLICATIONS
This application claims benefit of U.S. Patent Application Ser. No.
62/506,859 filed May 16, 2017, entitled "NON-UNIFORM OPTIMAL SURVEY
DESIGN PRINCIPLES," which is hereby incorporated by reference.
Claims
What is claimed is:
1. A method of acquiring seismic data comprising: a) determining a
non-uniform optimal sampling design by calculating a compressive
sensing sampling grid that minimizes mutual coherence; b) placing a
plurality of source lines or receiver lines at a non-uniform
optimal line interval; c) placing a plurality of receivers or nodes
at a non-uniform optimal receiver interval; d) towing a plurality
of streamers attached to a vessel, wherein the plurality of
streamers is spaced apart at non-uniform optimal intervals, wherein
the plurality of streamers is at least three streamers; e) firing a
plurality of shots from one or more seismic sources at non-uniform
optimal shot intervals; and f) acquiring seismic data via the
plurality of receivers or nodes.
2. The method of claim 1, wherein the seismic data is acquired via
land, ocean-bottom seismic, or marine survey.
3. The method of claim 1, wherein the seismic data is sampled below
Nyquist-Shannon limit.
4. The method of claim 1, further comprising: applying sparse
inversion-based reconstruction on the seismic data.
5. The method of claim 1, wherein the plurality of streamers ranges
from 6 to 50 streamers.
6. The method of claim 1, wherein each shot interval ranges from
about 5 m to about 100 m.
7. The method of claim 1, wherein each receiver interval ranges
from about 5 m to about 100 m.
8. The method of claim 1, wherein each streamer interval ranges
from about 25 m to about 200 m.
9. The method of claim 1, wherein each line interval ranges from
about 25 m to about 500 m.
10. The method of claim 1, further comprising: applying sparse
inversion-based deblending on the seismic data.
11. A method of acquiring seismic data comprising: determining a
non-uniform optimal sampling design by calculating a compressive
sensing sampling grid that minimizes mutual coherence, wherein
mutual coherence is determined by:
.mu..function..noteq..times..times..times..times..times..times.
##EQU00005## and mutual coherence is minimized by:
.times..mu..function..times..noteq..times. ##EQU00006## where S is
sparsity basis, R is a sampling operator, .mu. mutual coherence,
and {circumflex over (r)}.sub.l are Fourier coefficients of
diag(R*R); placing a plurality of source lines or receiver lines at
a non-uniform optimal line interval; placing a plurality of
receivers or nodes at a non-uniform optimal receiver interval;
towing a plurality of streamers attached to a vessel, wherein the
plurality of streamers is spaced apart at non-uniform optimal
intervals, wherein the plurality of streamers is at least three
streamers; firing a plurality of shots from one or more seismic
sources at non-uniform optimal shot intervals; and acquiring
seismic data via the plurality of receivers or nodes.
12. The method of claim 11, wherein the seismic data is acquired
via land, ocean-bottom seismic, or marine survey.
13. The method of claim 11, wherein the seismic data is sampled
below Nyquist-Shannon limit.
14. The method of claim 11, further comprising: applying sparse
inversion-based reconstruction on the seismic data.
15. The method of claim 11, wherein the plurality of streamers
ranges from 6 to 50 streamers.
16. The method of claim 11, wherein each shot interval ranges from
about 5 m to about 100 m.
17. The method of claim 11, wherein each receiver interval ranges
from about 5 m to about 100 m.
18. The method of claim 11, wherein each streamer interval ranges
from about 25 m to about 200 m.
19. The method of claim 11, wherein each line interval ranges from
about 25 m to about 500 m.
20. The method of claim 11, further comprising: applying sparse
inversion-based deblending on the seismic data.
Description
FIELD OF THE INVENTION
The present invention relates generally to seismic imaging. More
particularly, but not by way of limitation, embodiments of the
present invention include tools and methods for designing and
implementing seismic data acquisition using non-uniform optimal
sampling principles.
BACKGROUND OF THE INVENTION
Compressive sensing (CS) is an emerging field in signal processing
that has applications in many different disciplines including
seismic surveying. Traditionally, Nyquist-Shannon sampling theorem
established the sufficient condition for a sampling rate that
permits a digital signal to capture all the information from a
continuous-time signal of finite bandwidth. Compressive sensing
provides a new paradigm of sampling which requires far fewer
measurements compared to Nyquist-Shannon sampling criterion. Thus
far, compressive sensing theory suggests that successful signal
recovery can be best achieved through random measurements together
with sparsity of the true signal. However, applying random sampling
to seismic surveys raises many concerns and uncertainties.
BRIEF SUMMARY OF THE DISCLOSURE
The present invention relates generally to seismic imaging. More
particularly, but not by way of limitation, embodiments of the
present invention include tools and methods for designing and
implementing seismic data acquisition using non-uniform optimal
sampling principles.
One method of acquiring seismic data includes determining a
non-uniform optimal sampling design that includes a compressive
sensing sampling grid; placing a plurality of source lines or
receiver lines at a non-uniform optimal line interval; placing a
plurality of receivers or nodes at a non-uniform optimal receiver
interval; towing a plurality of streamers attached to a vessel,
wherein the plurality of streamers is spaced apart at non-uniform
optimal intervals based on the compressive sensing sampling grid;
firing a plurality of shots from one or more seismic sources at
non-uniform optimal shot intervals; and acquiring seismic data via
the plurality of receivers or nodes.
BRIEF DESCRIPTION OF THE DRAWINGS
A more complete understanding of the present invention and benefits
thereof may be acquired by referring to the follow description
taken in conjunction with the accompanying drawings in which:
FIGS. 1A-1B illustrate an embodiment of non-uniform optimal
sampling design as applied to a marine seismic survey utilizing 12
streamers. FIG. 1A shows a shot interval distribution from a single
gun. FIG. 1B shows cable configuration.
FIGS. 2A-2B illustrate an embodiment of non-uniform optimal
sampling design utilizing 16 streamers. FIG. 2A shows a shot
interval distribution. FIG. 2B shows cable configuration.
FIG. 3 illustrates an onboard quality control (QC) for continuous
records.
FIG. 4 illustrates implementation of non-uniform optimal sampling
shot spacing in the field.
FIGS. 5A-5B illustrate non-uniform optimal sampling shot design
statistics from a production survey. FIG. 5A shows a distribution
of shot intervals. FIG. 5B shows a distribution of shot time
intervals.
FIGS. 6A-6D illustrate a comparison of a non-uniform optimal
sampling shot design to a conventional regular design on deblending
quality. FIG. 6A shows data acquired with conventional regular
design. FIG. 6B shows corresponding deblending result of FIG. 6A.
FIG. 6C shows data acquired with a non-uniform optimal sampling
shot design. FIG. 6D shows corresponding deblending result of FIG.
6C.
DETAILED DESCRIPTION
Reference will now be made in detail to embodiments of the
invention, one or more examples of which are illustrated in the
accompanying drawings. Each example is provided by way of
explanation of the invention, not as a limitation of the invention.
It will be apparent to those skilled in the art that various
modifications and variations can be made in the present invention
without departing from the scope or spirit of the invention. For
instance, features illustrated or described as part of one
embodiment can be used on another embodiment to yield a still
further embodiment. Thus, it is intended that the present invention
cover such modifications and variations that come within the scope
of the invention.
In signal processing, compressive sensing (CS) asserts that the
exact recovery of certain signals can be obtained from far fewer
measurements than as required by Shannon's sampling criterion.
Generally speaking, applicability of compressive sensing for
imaging depends on sparsity of signals and incoherence of sampling
waveforms.
The present invention provides systems and methods for acquiring
seismic data with relatively few measurements by utilizing
compressive sensing principles. These principles include, but are
not limited to, non-uniform optimal sampling (NUOS) design, seismic
data reconstruction of data acquired using NUOS design, and blended
source acquisition with NUOS design. These principles have been
applied to real-world seismic survey scenarios including marine and
ocean bottom seismic (OBS) and land surveys to increase data
bandwidth and resolution.
Non-Uniform Optimal Sampling Design
One of the goals of non-uniform optimal sampling design is to find
an optimal sampling grid that favors seismic data reconstruction.
Non-uniform optimal sampling design provides a mathematical
framework for optimizing both source and receiver configuration
designs. As a summary, the following mathematical description of
non-uniform optimal sampling design is provided.
The forward model for seismic data reconstruction can be described
as b=Dx,b=RS*x,x=Su, (1) where b represents acquired seismic data
on an irregular observed grid and u represents reconstructed
seismic data on a finer regular reconstructed grid. The operator R
is a restriction/sampling operator, which maps data from the
reconstructed grid to the observed grid. If S is a suitably chosen
dictionary (possibly over-complete), x is a sparse representation
of u which has a small cardinality.
Mutual coherence is a measure of incoherency between sparsity basis
S and sampling operator R. A high-fidelity data reconstruction
requires the mutual coherence to be as small as possible. Assuming
D=RS* can be written in a matrix form and d.sub.i represent
different columns in D, the mutual coherence .mu. can be defined
as,
.mu..function..noteq..times..times..times..times..times..times.
##EQU00001## This is equivalent to the absolute maximum
off-diagonal element of the Gram matrix, G=D*D.
The relationship between mutual coherence and successful data
reconstruction is appealing for analysis. Typically, for seismic
applications, this type of analysis would be prohibitively
expensive to compute. However, if S is allowed to be a Fourier
transform, then the definition of mutual coherence in equation 2
can be simplified to
.mu..function..noteq..times. ##EQU00002## where {circumflex over
(r)}.sub.l are Fourier coefficients of diag(R*R). This can be
interpreted as finding the largest non-DC Fourier component of a
given sampling grid, which can be carried out efficiently using the
fast transform. Equation 3 can serve as a proxy for mutual
coherence when S is some over-complete dictionary, such as curvelet
and generalized windowed Fourier transform (GWT).
Given the estimate for mutual coherence in equation 3, the
non-uniform optimal sampling design seeks a sampling grid which
minimizes the mutual coherence as follows,
.times..mu..function..times..noteq..times. ##EQU00003##
The optimization problem in equation 4 can be effectively solved
by, for example randomized greedy algorithms such as GRASP (Feo and
Resende, 1995). In practice, the non-uniform optimal sampling
design can be applied to both source and receiver sides.
Seismic Data Reconstruction
Seismic data acquired from the non-uniform optimal sampling design
can be reconstructed to a finer grid by solving an analysis-based
basis pursuit denoising problem:
.times..times..times..times..ltoreq..sigma. ##EQU00004## Here
.sigma. is some approximation of noise level in the acquired data
b. While conventional interpolation techniques focus on filling in
acquisition holes or increasing fold, CS-based data reconstruction
improves sampling and extends unaliased bandwidth. Seismic data
must be acquired in an irregular fashion in order to employ
CS-based data reconstruction. Ideally with a proper non-uniform
optimal sampling design, we can increase the unaliased bandwidth by
a factor of 2-4 in a certain direction.
EXAMPLE 1
A production streamer survey is described in this example to
illustrate design and reconstruction of marine seismic data in
accordance with the present invention. A vessel equipped with a
flip-flop source shooting every 18.75 m (on average) was used to
acquire 3D streamer survey. Total of 12 streamers were towed behind
the vessel. Each streamer was 5 km in length and 600 m in spread
width.
Non-uniform optimal sampling source design was utilized to improve
in-line sampling. Non-uniform optimal sampling cable design was
utilized to improve cross-line sampling. Design considerations
include, but are not limited to, minimum airgun cycle time, minimum
cable separation, spread balancing, and the like. FIGS. 1A-1B
illustrates non-uniform optimal sampling design principles as
applied to a 12 cable configuration. Referring to FIG. 1A, a shot
interval distribution from a single gun according to an embodiment
is plotted. While FIG. 1A shows shot interval ranging from about 25
m to 50 m, other distance ranges may be consistent with NUOS design
depending on a number of factors such as the cable configuration.
FIG. 1B shows a cable configuration according to an embodiment. As
shown, the cable interval may have non-uniform spacing (ranging
from about 25 m to about 200 m). FIGS. 2A-2B illustrate non-uniform
optimal sampling design principles as applied to a 16 cable
configuration. As shown in FIG. 2A, the shot interval may range
from about 10 m to about 31 m. In some embodiments, the shot
interval may range from about 5 m to about 100 m. FIG. 2B shows
non-uniform spacing of a 16 cable configuration in accordance with
an embodiment.
Blended Source Acquisition
In conventional seismic data acquisition, sources are activated
with adequate time intervals to ensure no interference between
adjacent sources. The acquisition efficiency is limited by
equipment and operational constraints. In particular, the source
side sampling is often coarse and aliased if long record lengths
are needed to obtain energy from far offsets.
In blended source acquisition, multiple sources may be activated
within a single conventional shotpoint time window. Overlapping
sources in time allows dramatic reduction in time associated with
acquisition. It can also improve spatial sampling by increasing
shot density. The tradeoff is that sources are blended together and
generate so-called "blending noise". The process of separating
sources and forming interference-free records is commonly referred
to as "deblending."
For marine towed streamer and ocean bottom seismic (OBS), blended
source acquisition can be carried out using multiple source vessels
shooting simultaneously, or a single source vessel firing at a
short time interval. Early marine simultaneous source experiment
used an extra source vessel sailing behind the streamer vessel. Two
sources were distance-separated and F-K filter was applied to
separate shots. Later on, the concept of introducing small random
time delays between each pair of sources was developed. Under this
time-dithering scheme, interference between two sources became
asynchronous incoherent noise and could be suppressed during
conventional pre-stack time migration. Recent developments proposed
the time-scheduling method for OBS which required little
coordination between sources. Each source was assigned a set of
random source initiation times and shots were taken following these
times.
Both time-dithering and time-scheduling methods required extra
manipulation of shot time and sometimes even vessel speed, which
further complicates field operation and lead to potential human
errors. Blended source acquisition can also be applied to NUOS. The
NUOS scheme puts no constraints on shot time and makes minimal
operational changes compared to conventional seismic acquisition.
Both sampling density and deblending quality can benefit from a
joint inversion of data acquired using a NUOS design.
For blended source acquisition, the recording system should be
capable of recording continuously. Data should be delivered in a
format of continuous records instead of conventional shot gathers.
Each continuous record or time segment is expected to contain
receives information and record start and end time stamps within at
least microsecond precision. The source positioning data together
with shot times can be stored in navigation files modified from one
of the standard formats (e.g., SPS, P1/90, P1/11, etc). To better
assist inversion-based deblending, time stamps from all shots
should be recorded including production, non-production and infill
shots, also within at least microsecond precision.
Routine onboard QC procedures can still be employed. Continuous
records can be examined onboard by displaying the "time-segment
gather" (i.e., data within a certain time window sorted by
receivers). In this domain, blended shots are observed as coherent
energy, regardless of uniform or non-uniform shooting patterns.
FIG. 3 illustrates a snapshot of onboard QC, showing a time-segment
gather over the entire receiver patch. The opposite-trending
moveouts indicate shots that were activated from two distanced
sources. This survey employed dual-vessel simultaneous shooting
with NUOS design and led to a reduction in overall survey time,
including time for receiver deployment, mobilization and
demobilization. Onboard processing was kept to a minimum to avoid
damaging the integrity of the continuous records.
Cs-Based Survey Design Principle
Separating blended sources can be better solved under a CS
framework. Forward solutions have been proposed by exploiting the
sparsity of seismic data, such as the generalized windowed Fourier.
The non-uniform sampling scheme favors the inversion-based
deblending by promoting the incoherence of blending noise. For
seismic acquisition, a measure of incoherence ("mutual coherence")
is used to guide the non-uniform survey design. Referring back to
equations 2-4, a proxy of mutual coherence can be effectively
computed using the Fourier transform. Non-uniform optimal sampling
minimizes mutual coherence to obtain an optimal survey design.
EXAMPLE 2
A field trial was conducted in the early stage of development. FIG.
4 illustrates an aspect of the field trial. Each red dot represents
a pre-plot shot location derived from the optimization process, and
each red box represents a shot point in the field. Through the
course of the field trial, 0.5 m inline accuracy was achieved for
99:6% shots. The field trial removed barriers to implementing NUOS
design on shots in production surveys.
For blended source acquisition, we rely on the non-uniform design
in space, which by nature gives rise to irregularity in time, to
generate the incoherent blending pattern needed for source
separation. FIGS. 5A-5B show statistics from a production survey
designed with non-uniform optimal sampling shot spacing. FIG. 5A
plots a distribution of shot intervals that ranged from 15 m to 35
m. FIG. 5B plots a distribution of rendered shot time intervals
that ranged from 6 s to 14 s.
FIGS. 6A-6D compare data acquired with a NUOS design and a
conventional regular design, both from the same survey. Fifteen
seconds record length was kept to preserve far offsets and
converted waves. FIG. 6A shows a receiver gather, as part of a
velocity line, with shots spaced at regular 25 m intervals. As
shown, self-blending occurred after 10 s. The interference pattern
was somewhat incoherent even with a regular shot spacing, thanks to
natural variations in vessel speed. FIG. 6C shows the same receiver
with production shots optimally spaced at nominal 25 m intervals.
The interference from self-blending came in as early as 7.5 s and
spread over a longer time interval. The incoherence of blending
noise was significantly enhanced by the NUOS design.
The same inversion-based deblending method was applied on both
datasets for a fair comparison. The method solves an analysis-based
t.sub.l minimization using the nonmonotone ADM (Li et al., 2013b).
FIGS. 6B and 6D show the corresponding deblending results. For data
with a regular design, we see a fair amount of blending noise
leaked through deblending, due to insufficient incoherence to
separate signal from noise. On the other hand, a much improved
deblending result was achieved from data with a NUOS design. The
blending noise was reduced to a minimum while primaries were
intact. This result indicates that the NUOS design was preferable
for the inversion-based deblending method. A similar conclusion has
been observed from dual-vessel simultaneous shooting.
Although the systems and processes described herein have been
described in detail, it should be understood that various changes,
substitutions, and alterations can be made without departing from
the spirit and scope of the invention as defined by the following
claims. Those skilled in the art may be able to study the preferred
embodiments and identify other ways to practice the invention that
are not exactly as described herein. It is the intent of the
inventors that variations and equivalents of the invention are
within the scope of the claims while the description, abstract and
drawings are not to be used to limit the scope of the invention.
The invention is specifically intended to be as broad as the claims
below and their equivalents.
* * * * *